Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations20040
Missing cells4584
Missing cells (%)0.8%
Duplicate rows40
Duplicate rows (%)0.2%
Total size in memory12.9 MiB
Average record size in memory675.5 B

Variable types

Numeric16
DateTime2
Categorical12

Dataset

DescriptionComprehensive profiling of the telecom subscriber dataset.
URL

Alerts

Dataset has 40 (0.2%) duplicate rowsDuplicates
credit_score is highly overall correlated with late_paymentsHigh correlation
lat is highly overall correlated with provinceHigh correlation
late_payments is highly overall correlated with credit_scoreHigh correlation
lng is highly overall correlated with provinceHigh correlation
monthly_charges is highly overall correlated with next_month_spend and 1 other fieldsHigh correlation
next_month_spend is highly overall correlated with monthly_charges and 2 other fieldsHigh correlation
plan_type is highly overall correlated with next_month_spendHigh correlation
province is highly overall correlated with lat and 1 other fieldsHigh correlation
review_text is highly overall correlated with satisfaction_scoreHigh correlation
satisfaction_score is highly overall correlated with review_textHigh correlation
tenure_months is highly overall correlated with total_chargesHigh correlation
total_charges is highly overall correlated with monthly_charges and 2 other fieldsHigh correlation
satisfaction_score is highly imbalanced (57.2%)Imbalance
payment_method has 399 (2.0%) missing valuesMissing
data_usage_gb has 778 (3.9%) missing valuesMissing
avg_session_minutes has 658 (3.3%) missing valuesMissing
credit_score has 969 (4.8%) missing valuesMissing
income has 1576 (7.9%) missing valuesMissing
review_text has 204 (1.0%) missing valuesMissing
customer_id is uniformly distributedUniform
tenure_months has 564 (2.8%) zerosZeros
total_charges has 564 (2.8%) zerosZeros
support_tickets_last_6mo has 8323 (41.5%) zerosZeros
late_payments has 12384 (61.8%) zerosZeros

Reproduction

Analysis started2025-11-02 21:25:08.299101
Analysis finished2025-11-02 21:25:26.904286
Duration18.61 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Uniform 

Distinct20000
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10004.174
Minimum1
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:26.950499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1002.95
Q15004.75
median10004.5
Q315006.25
95-th percentile19001.05
Maximum20000
Range19999
Interquartile range (IQR)10001.5

Descriptive statistics

Standard deviation5773.6797
Coefficient of variation (CV)0.5771271
Kurtosis-1.2001999
Mean10004.174
Median Absolute Deviation (MAD)5001
Skewness-0.00068560945
Sum2.0048364 × 108
Variance33335377
MonotonicityNot monotonic
2025-11-02T23:25:27.016605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93022
 
< 0.1%
34392
 
< 0.1%
27452
 
< 0.1%
134372
 
< 0.1%
123872
 
< 0.1%
73572
 
< 0.1%
25592
 
< 0.1%
91082
 
< 0.1%
67212
 
< 0.1%
114682
 
< 0.1%
Other values (19990)20020
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
200001
< 0.1%
199991
< 0.1%
199981
< 0.1%
199971
< 0.1%
199961
< 0.1%
199951
< 0.1%
199941
< 0.1%
199931
< 0.1%
199921
< 0.1%
199911
< 0.1%
Distinct2555
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size156.7 KiB
Minimum2018-01-01 00:00:00
Maximum2024-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-02T23:25:27.174835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:27.252556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2682
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size156.7 KiB
Minimum2018-01-09 00:00:00
Maximum2025-09-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-02T23:25:27.344568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:27.422245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

age
Real number (ℝ)

Distinct54
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.695609
Minimum18
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:27.489388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q128
median34
Q341
95-th percentile51
Maximum77
Range59
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.5703269
Coefficient of variation (CV)0.27583684
Kurtosis-0.37688158
Mean34.695609
Median Absolute Deviation (MAD)7
Skewness0.23206646
Sum695300
Variance91.591158
MonotonicityNot monotonic
2025-11-02T23:25:27.557540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181084
 
5.4%
34863
 
4.3%
36792
 
4.0%
32781
 
3.9%
33773
 
3.9%
35773
 
3.9%
37772
 
3.9%
38761
 
3.8%
31740
 
3.7%
39690
 
3.4%
Other values (44)12011
59.9%
ValueCountFrequency (%)
181084
5.4%
19251
 
1.3%
20248
 
1.2%
21348
 
1.7%
22328
 
1.6%
23415
 
2.1%
24483
2.4%
25520
2.6%
26577
2.9%
27662
3.3%
ValueCountFrequency (%)
771
 
< 0.1%
751
 
< 0.1%
692
 
< 0.1%
681
 
< 0.1%
672
 
< 0.1%
669
 
< 0.1%
652
 
< 0.1%
647
 
< 0.1%
6310
< 0.1%
6224
0.1%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Female
9778 
Male
9533 
Other
 
729

Length

Max length6
Median length5
Mean length5.0122255
Min length4

Characters and Unicode

Total characters100445
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female9778
48.8%
Male9533
47.6%
Other729
 
3.6%

Length

2025-11-02T23:25:27.625821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-02T23:25:27.673573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female9778
48.8%
male9533
47.6%
other729
 
3.6%

Most occurring characters

ValueCountFrequency (%)
e29818
29.7%
a19311
19.2%
l19311
19.2%
F9778
 
9.7%
m9778
 
9.7%
M9533
 
9.5%
O729
 
0.7%
t729
 
0.7%
h729
 
0.7%
r729
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)100445
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e29818
29.7%
a19311
19.2%
l19311
19.2%
F9778
 
9.7%
m9778
 
9.7%
M9533
 
9.5%
O729
 
0.7%
t729
 
0.7%
h729
 
0.7%
r729
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100445
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e29818
29.7%
a19311
19.2%
l19311
19.2%
F9778
 
9.7%
m9778
 
9.7%
M9533
 
9.5%
O729
 
0.7%
t729
 
0.7%
h729
 
0.7%
r729
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100445
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e29818
29.7%
a19311
19.2%
l19311
19.2%
F9778
 
9.7%
m9778
 
9.7%
M9533
 
9.5%
O729
 
0.7%
t729
 
0.7%
h729
 
0.7%
r729
 
0.7%

province
Categorical

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Harare
2086 
Mashonaland East
2044 
Mashonaland West
2034 
Masvingo
2023 
Matabeleland South
1999 
Other values (5)
9854 

Length

Max length19
Median length18
Mean length12.65983
Min length6

Characters and Unicode

Total characters253703
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMatabeleland North
2nd rowMashonaland West
3rd rowMidlands
4th rowBulawayo
5th rowMatabeleland South

Common Values

ValueCountFrequency (%)
Harare2086
10.4%
Mashonaland East2044
10.2%
Mashonaland West2034
10.1%
Masvingo2023
10.1%
Matabeleland South1999
10.0%
Bulawayo1995
10.0%
Manicaland1982
9.9%
Midlands1973
9.8%
Matabeleland North1967
9.8%
Mashonaland Central1937
9.7%

Length

2025-11-02T23:25:27.720026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-02T23:25:27.779420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mashonaland6015
20.0%
matabeleland3966
13.2%
harare2086
 
6.9%
east2044
 
6.8%
west2034
 
6.8%
masvingo2023
 
6.7%
south1999
 
6.7%
bulawayo1995
 
6.6%
manicaland1982
 
6.6%
midlands1973
 
6.6%
Other values (2)3904
13.0%

Most occurring characters

ValueCountFrequency (%)
a52028
20.5%
n25893
10.2%
l21834
 
8.6%
M15959
 
6.3%
d15909
 
6.3%
s14089
 
5.6%
o13999
 
5.5%
e13989
 
5.5%
t13947
 
5.5%
9981
 
3.9%
Other values (17)56075
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)253703
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a52028
20.5%
n25893
10.2%
l21834
 
8.6%
M15959
 
6.3%
d15909
 
6.3%
s14089
 
5.6%
o13999
 
5.5%
e13989
 
5.5%
t13947
 
5.5%
9981
 
3.9%
Other values (17)56075
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)253703
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a52028
20.5%
n25893
10.2%
l21834
 
8.6%
M15959
 
6.3%
d15909
 
6.3%
s14089
 
5.6%
o13999
 
5.5%
e13989
 
5.5%
t13947
 
5.5%
9981
 
3.9%
Other values (17)56075
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)253703
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a52028
20.5%
n25893
10.2%
l21834
 
8.6%
M15959
 
6.3%
d15909
 
6.3%
s14089
 
5.6%
o13999
 
5.5%
e13989
 
5.5%
t13947
 
5.5%
9981
 
3.9%
Other values (17)56075
22.1%

lat
Real number (ℝ)

High correlation 

Distinct15914
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-18.879823
Minimum-21.6306
Maximum-16.1507
Zeros0
Zeros (%)0.0%
Negative20040
Negative (%)100.0%
Memory size156.7 KiB
2025-11-02T23:25:27.888745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-21.6306
5-th percentile-21.05431
Q1-19.968725
median-18.82895
Q3-17.827275
95-th percentile-16.767825
Maximum-16.1507
Range5.4799
Interquartile range (IQR)2.14145

Descriptive statistics

Standard deviation1.2868369
Coefficient of variation (CV)-0.068159372
Kurtosis-0.90675311
Mean-18.879823
Median Absolute Deviation (MAD)1.08305
Skewness0.0033369104
Sum-378351.65
Variance1.6559491
MonotonicityNot monotonic
2025-11-02T23:25:27.981526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18.91297
 
< 0.1%
-20.03476
 
< 0.1%
-18.68746
 
< 0.1%
-18.6316
 
< 0.1%
-18.57236
 
< 0.1%
-18.62035
 
< 0.1%
-20.18725
 
< 0.1%
-18.50645
 
< 0.1%
-17.58195
 
< 0.1%
-20.00595
 
< 0.1%
Other values (15904)19984
99.7%
ValueCountFrequency (%)
-21.63061
< 0.1%
-21.62561
< 0.1%
-21.61331
< 0.1%
-21.60491
< 0.1%
-21.60461
< 0.1%
-21.60121
< 0.1%
-21.57851
< 0.1%
-21.57351
< 0.1%
-21.55941
< 0.1%
-21.55631
< 0.1%
ValueCountFrequency (%)
-16.15071
< 0.1%
-16.15091
< 0.1%
-16.16271
< 0.1%
-16.16961
< 0.1%
-16.18181
< 0.1%
-16.20091
< 0.1%
-16.20191
< 0.1%
-16.23691
< 0.1%
-16.24971
< 0.1%
-16.28011
< 0.1%

lng
Real number (ℝ)

High correlation 

Distinct16056
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.313769
Minimum26.7921
Maximum33.3467
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:28.061460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.7921
5-th percentile27.5528
Q129.072375
median30.53245
Q331.213525
95-th percentile32.667315
Maximum33.3467
Range6.5546
Interquartile range (IQR)2.14115

Descriptive statistics

Standard deviation1.516646
Coefficient of variation (CV)0.050031587
Kurtosis-0.79024401
Mean30.313769
Median Absolute Deviation (MAD)1.08415
Skewness-0.20191754
Sum607487.93
Variance2.300215
MonotonicityNot monotonic
2025-11-02T23:25:28.140315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.88187
 
< 0.1%
30.98756
 
< 0.1%
30.99295
 
< 0.1%
31.33055
 
< 0.1%
29.98945
 
< 0.1%
30.98095
 
< 0.1%
30.87045
 
< 0.1%
30.99385
 
< 0.1%
31.08415
 
< 0.1%
31.15235
 
< 0.1%
Other values (16046)19987
99.7%
ValueCountFrequency (%)
26.79211
< 0.1%
26.83131
< 0.1%
26.92241
< 0.1%
26.96811
< 0.1%
26.96871
< 0.1%
26.98021
< 0.1%
26.99981
< 0.1%
27.01261
< 0.1%
27.01371
< 0.1%
27.02561
< 0.1%
ValueCountFrequency (%)
33.34671
< 0.1%
33.31951
< 0.1%
33.25551
< 0.1%
33.25531
< 0.1%
33.25291
< 0.1%
33.25231
< 0.1%
33.24731
< 0.1%
33.24161
< 0.1%
33.21611
< 0.1%
33.19211
< 0.1%

plan_type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Prepaid
8927 
Postpaid
8064 
Premium
3049 

Length

Max length8
Median length7
Mean length7.4023952
Min length7

Characters and Unicode

Total characters148344
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPremium
2nd rowPrepaid
3rd rowPrepaid
4th rowPrepaid
5th rowPostpaid

Common Values

ValueCountFrequency (%)
Prepaid8927
44.5%
Postpaid8064
40.2%
Premium3049
 
15.2%

Length

2025-11-02T23:25:28.203490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-02T23:25:28.240200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
prepaid8927
44.5%
postpaid8064
40.2%
premium3049
 
15.2%

Most occurring characters

ValueCountFrequency (%)
P20040
13.5%
i20040
13.5%
a16991
11.5%
p16991
11.5%
d16991
11.5%
r11976
8.1%
e11976
8.1%
o8064
5.4%
s8064
5.4%
t8064
5.4%
Other values (2)9147
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)148344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P20040
13.5%
i20040
13.5%
a16991
11.5%
p16991
11.5%
d16991
11.5%
r11976
8.1%
e11976
8.1%
o8064
5.4%
s8064
5.4%
t8064
5.4%
Other values (2)9147
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)148344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P20040
13.5%
i20040
13.5%
a16991
11.5%
p16991
11.5%
d16991
11.5%
r11976
8.1%
e11976
8.1%
o8064
5.4%
s8064
5.4%
t8064
5.4%
Other values (2)9147
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)148344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P20040
13.5%
i20040
13.5%
a16991
11.5%
p16991
11.5%
d16991
11.5%
r11976
8.1%
e11976
8.1%
o8064
5.4%
s8064
5.4%
t8064
5.4%
Other values (2)9147
6.2%

contract
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Month-to-Month
11068 
One Year
5968 
Two Year
3004 

Length

Max length14
Median length14
Mean length11.313772
Min length8

Characters and Unicode

Total characters226728
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonth-to-Month
2nd rowMonth-to-Month
3rd rowOne Year
4th rowMonth-to-Month
5th rowTwo Year

Common Values

ValueCountFrequency (%)
Month-to-Month11068
55.2%
One Year5968
29.8%
Two Year3004
 
15.0%

Length

2025-11-02T23:25:28.305607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-02T23:25:28.343937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
month-to-month11068
38.1%
year8972
30.9%
one5968
20.6%
two3004
 
10.4%

Most occurring characters

ValueCountFrequency (%)
o36208
16.0%
t33204
14.6%
n28104
12.4%
M22136
9.8%
h22136
9.8%
-22136
9.8%
e14940
6.6%
8972
 
4.0%
Y8972
 
4.0%
a8972
 
4.0%
Other values (4)20948
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)226728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o36208
16.0%
t33204
14.6%
n28104
12.4%
M22136
9.8%
h22136
9.8%
-22136
9.8%
e14940
6.6%
8972
 
4.0%
Y8972
 
4.0%
a8972
 
4.0%
Other values (4)20948
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)226728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o36208
16.0%
t33204
14.6%
n28104
12.4%
M22136
9.8%
h22136
9.8%
-22136
9.8%
e14940
6.6%
8972
 
4.0%
Y8972
 
4.0%
a8972
 
4.0%
Other values (4)20948
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)226728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o36208
16.0%
t33204
14.6%
n28104
12.4%
M22136
9.8%
h22136
9.8%
-22136
9.8%
e14940
6.6%
8972
 
4.0%
Y8972
 
4.0%
a8972
 
4.0%
Other values (4)20948
9.2%

payment_method
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing399
Missing (%)2.0%
Memory size1.3 MiB
Debit Card
6803 
EcoCash
6094 
Credit Card
4901 
Cash
1843 

Length

Max length11
Median length10
Mean length8.7557151
Min length4

Characters and Unicode

Total characters171971
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit Card
2nd rowEcoCash
3rd rowEcoCash
4th rowDebit Card
5th rowEcoCash

Common Values

ValueCountFrequency (%)
Debit Card6803
33.9%
EcoCash6094
30.4%
Credit Card4901
24.5%
Cash1843
 
9.2%
(Missing)399
 
2.0%

Length

2025-11-02T23:25:28.396964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-02T23:25:28.440142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
card11704
37.3%
debit6803
21.7%
ecocash6094
19.4%
credit4901
15.6%
cash1843
 
5.9%

Most occurring characters

ValueCountFrequency (%)
C24542
14.3%
a19641
11.4%
r16605
9.7%
d16605
9.7%
t11704
 
6.8%
i11704
 
6.8%
e11704
 
6.8%
11704
 
6.8%
h7937
 
4.6%
s7937
 
4.6%
Other values (5)31888
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)171971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C24542
14.3%
a19641
11.4%
r16605
9.7%
d16605
9.7%
t11704
 
6.8%
i11704
 
6.8%
e11704
 
6.8%
11704
 
6.8%
h7937
 
4.6%
s7937
 
4.6%
Other values (5)31888
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)171971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C24542
14.3%
a19641
11.4%
r16605
9.7%
d16605
9.7%
t11704
 
6.8%
i11704
 
6.8%
e11704
 
6.8%
11704
 
6.8%
h7937
 
4.6%
s7937
 
4.6%
Other values (5)31888
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)171971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C24542
14.3%
a19641
11.4%
r16605
9.7%
d16605
9.7%
t11704
 
6.8%
i11704
 
6.8%
e11704
 
6.8%
11704
 
6.8%
h7937
 
4.6%
s7937
 
4.6%
Other values (5)31888
18.5%

device_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Android
12012 
iOS
5598 
Web
2430 

Length

Max length7
Median length7
Mean length5.3976048
Min length3

Characters and Unicode

Total characters108168
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAndroid
2nd rowAndroid
3rd rowAndroid
4th rowAndroid
5th rowiOS

Common Values

ValueCountFrequency (%)
Android12012
59.9%
iOS5598
27.9%
Web2430
 
12.1%

Length

2025-11-02T23:25:28.507313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-02T23:25:28.546360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
android12012
59.9%
ios5598
27.9%
web2430
 
12.1%

Most occurring characters

ValueCountFrequency (%)
d24024
22.2%
i17610
16.3%
A12012
11.1%
n12012
11.1%
r12012
11.1%
o12012
11.1%
O5598
 
5.2%
S5598
 
5.2%
W2430
 
2.2%
e2430
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)108168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d24024
22.2%
i17610
16.3%
A12012
11.1%
n12012
11.1%
r12012
11.1%
o12012
11.1%
O5598
 
5.2%
S5598
 
5.2%
W2430
 
2.2%
e2430
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)108168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d24024
22.2%
i17610
16.3%
A12012
11.1%
n12012
11.1%
r12012
11.1%
o12012
11.1%
O5598
 
5.2%
S5598
 
5.2%
W2430
 
2.2%
e2430
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)108168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d24024
22.2%
i17610
16.3%
A12012
11.1%
n12012
11.1%
r12012
11.1%
o12012
11.1%
O5598
 
5.2%
S5598
 
5.2%
W2430
 
2.2%
e2430
 
2.2%

has_app
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
15549 
0
4491 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20040
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
115549
77.6%
04491
 
22.4%

Length

2025-11-02T23:25:28.590240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-02T23:25:28.618472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
115549
77.6%
04491
 
22.4%

Most occurring characters

ValueCountFrequency (%)
115549
77.6%
04491
 
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
115549
77.6%
04491
 
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
115549
77.6%
04491
 
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
115549
77.6%
04491
 
22.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
16424 
1
3616 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20040
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
016424
82.0%
13616
 
18.0%

Length

2025-11-02T23:25:28.659214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-02T23:25:28.693672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
016424
82.0%
13616
 
18.0%

Most occurring characters

ValueCountFrequency (%)
016424
82.0%
13616
 
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
016424
82.0%
13616
 
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
016424
82.0%
13616
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
016424
82.0%
13616
 
18.0%

tenure_months
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.640419
Minimum0
Maximum36
Zeros564
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:28.737886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median16
Q325
95-th percentile33
Maximum36
Range36
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.9751897
Coefficient of variation (CV)0.59945543
Kurtosis-1.0437911
Mean16.640419
Median Absolute Deviation (MAD)8
Skewness0.15020363
Sum333474
Variance99.504409
MonotonicityNot monotonic
2025-11-02T23:25:28.811872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
11723
 
3.6%
10711
 
3.5%
12688
 
3.4%
9668
 
3.3%
14662
 
3.3%
13660
 
3.3%
16649
 
3.2%
17639
 
3.2%
15637
 
3.2%
18612
 
3.1%
Other values (27)13391
66.8%
ValueCountFrequency (%)
0564
2.8%
1496
2.5%
2528
2.6%
3582
2.9%
4551
2.7%
5551
2.7%
6535
2.7%
7575
2.9%
8576
2.9%
9668
3.3%
ValueCountFrequency (%)
36203
1.0%
35399
2.0%
34374
1.9%
33381
1.9%
32407
2.0%
31474
2.4%
30475
2.4%
29480
2.4%
28463
2.3%
27468
2.3%

monthly_charges
Real number (ℝ)

High correlation 

Distinct6408
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.634177
Minimum5
Maximum685.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:28.883701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.3695
Q115.47
median25.305
Q340.67
95-th percentile78.4005
Maximum685.08
Range680.08
Interquartile range (IQR)25.2

Descriptive statistics

Standard deviation22.205095
Coefficient of variation (CV)0.70193368
Kurtosis51.423338
Mean31.634177
Median Absolute Deviation (MAD)11.225
Skewness3.2398279
Sum633948.91
Variance493.06622
MonotonicityNot monotonic
2025-11-02T23:25:28.953051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
538
 
0.2%
15.218
 
0.1%
14.6618
 
0.1%
16.5918
 
0.1%
14.9218
 
0.1%
15.5118
 
0.1%
14.717
 
0.1%
14.3517
 
0.1%
13.3817
 
0.1%
14.7317
 
0.1%
Other values (6398)19844
99.0%
ValueCountFrequency (%)
538
0.2%
5.021
 
< 0.1%
5.031
 
< 0.1%
5.061
 
< 0.1%
5.11
 
< 0.1%
5.121
 
< 0.1%
5.181
 
< 0.1%
5.191
 
< 0.1%
5.231
 
< 0.1%
5.241
 
< 0.1%
ValueCountFrequency (%)
685.081
< 0.1%
492.181
< 0.1%
312.781
< 0.1%
300.31
< 0.1%
258.061
< 0.1%
238.51
< 0.1%
220.081
< 0.1%
219.361
< 0.1%
213.721
< 0.1%
201.481
< 0.1%

total_charges
Real number (ℝ)

High correlation  Zeros 

Distinct17759
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean524.17739
Minimum0
Maximum4214.07
Zeros564
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:29.107489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.819
Q1177.0725
median366.55
Q3697.2475
95-th percentile1575.3925
Maximum4214.07
Range4214.07
Interquartile range (IQR)520.175

Descriptive statistics

Standard deviation513.14362
Coefficient of variation (CV)0.97895031
Kurtosis4.8111329
Mean524.17739
Median Absolute Deviation (MAD)227.575
Skewness1.9466897
Sum10504515
Variance263316.37
MonotonicityNot monotonic
2025-11-02T23:25:29.166611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0564
 
2.8%
414.164
 
< 0.1%
659.694
 
< 0.1%
4274
 
< 0.1%
356.124
 
< 0.1%
510.174
 
< 0.1%
396.824
 
< 0.1%
32.174
 
< 0.1%
248.844
 
< 0.1%
67.354
 
< 0.1%
Other values (17749)19440
97.0%
ValueCountFrequency (%)
0564
2.8%
4.681
 
< 0.1%
5.631
 
< 0.1%
6.691
 
< 0.1%
6.821
 
< 0.1%
7.031
 
< 0.1%
7.71
 
< 0.1%
7.851
 
< 0.1%
8.021
 
< 0.1%
8.41
 
< 0.1%
ValueCountFrequency (%)
4214.071
< 0.1%
3878.791
< 0.1%
3826.041
< 0.1%
3647.731
< 0.1%
3637.531
< 0.1%
3615.171
< 0.1%
3601.671
< 0.1%
3569.31
< 0.1%
3537.051
< 0.1%
3509.661
< 0.1%

support_tickets_last_6mo
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89510978
Minimum0
Maximum8
Zeros8323
Zeros (%)41.5%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:29.210886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.96512159
Coefficient of variation (CV)1.0782159
Kurtosis1.5746834
Mean0.89510978
Median Absolute Deviation (MAD)1
Skewness1.1460036
Sum17938
Variance0.93145969
MonotonicityNot monotonic
2025-11-02T23:25:29.252212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
08323
41.5%
17168
35.8%
23269
 
16.3%
3970
 
4.8%
4248
 
1.2%
546
 
0.2%
613
 
0.1%
72
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
08323
41.5%
17168
35.8%
23269
 
16.3%
3970
 
4.8%
4248
 
1.2%
546
 
0.2%
613
 
0.1%
72
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
72
 
< 0.1%
613
 
0.1%
546
 
0.2%
4248
 
1.2%
3970
 
4.8%
23269
 
16.3%
17168
35.8%
08323
41.5%

data_usage_gb
Real number (ℝ)

Missing 

Distinct2576
Distinct (%)13.4%
Missing778
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean9.8799663
Minimum0.21
Maximum223.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:29.309337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile2.65
Q15.67
median8.885
Q312.89
95-th percentile20.33
Maximum223.52
Range223.31
Interquartile range (IQR)7.22

Descriptive statistics

Standard deviation6.4050209
Coefficient of variation (CV)0.64828368
Kurtosis160.33966
Mean9.8799663
Median Absolute Deviation (MAD)3.505
Skewness6.6531955
Sum190307.91
Variance41.024293
MonotonicityNot monotonic
2025-11-02T23:25:29.386709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.4331
 
0.2%
6.0426
 
0.1%
5.1826
 
0.1%
10.3725
 
0.1%
6.2524
 
0.1%
8.4324
 
0.1%
9.2124
 
0.1%
3.8623
 
0.1%
6.2123
 
0.1%
4.4223
 
0.1%
Other values (2566)19013
94.9%
(Missing)778
 
3.9%
ValueCountFrequency (%)
0.211
< 0.1%
0.221
< 0.1%
0.231
< 0.1%
0.331
< 0.1%
0.351
< 0.1%
0.371
< 0.1%
0.381
< 0.1%
0.391
< 0.1%
0.41
< 0.1%
0.451
< 0.1%
ValueCountFrequency (%)
223.521
< 0.1%
195.761
< 0.1%
184.961
< 0.1%
158.081
< 0.1%
127.681
< 0.1%
115.841
< 0.1%
99.521
< 0.1%
82.241
< 0.1%
77.041
< 0.1%
76.081
< 0.1%

calls_per_month
Real number (ℝ)

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.013323
Minimum16
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:29.450153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile31
Q137
median43
Q349
95-th percentile62
Maximum90
Range74
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.3544554
Coefficient of variation (CV)0.2125369
Kurtosis0.56160165
Mean44.013323
Median Absolute Deviation (MAD)6
Skewness0.70657745
Sum882027
Variance87.505836
MonotonicityNot monotonic
2025-11-02T23:25:29.514459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39973
 
4.9%
42972
 
4.9%
41953
 
4.8%
40942
 
4.7%
37922
 
4.6%
38883
 
4.4%
43881
 
4.4%
44851
 
4.2%
45834
 
4.2%
36786
 
3.9%
Other values (60)11043
55.1%
ValueCountFrequency (%)
161
 
< 0.1%
181
 
< 0.1%
203
 
< 0.1%
213
 
< 0.1%
2212
 
0.1%
2320
 
0.1%
2423
 
0.1%
2547
0.2%
2672
0.4%
2793
0.5%
ValueCountFrequency (%)
901
 
< 0.1%
881
 
< 0.1%
871
 
< 0.1%
843
 
< 0.1%
832
 
< 0.1%
823
 
< 0.1%
816
< 0.1%
802
 
< 0.1%
7912
0.1%
788
< 0.1%

messages_per_month
Real number (ℝ)

Distinct80
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.97475
Minimum43
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:29.580734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile69
Q179
median86
Q393
95-th percentile103
Maximum139
Range96
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.467369
Coefficient of variation (CV)0.12174934
Kurtosis-0.046172277
Mean85.97475
Median Absolute Deviation (MAD)7
Skewness0.05650414
Sum1722934
Variance109.56581
MonotonicityNot monotonic
2025-11-02T23:25:29.646935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87787
 
3.9%
85758
 
3.8%
86753
 
3.8%
81745
 
3.7%
84745
 
3.7%
90741
 
3.7%
88708
 
3.5%
83700
 
3.5%
89692
 
3.5%
82664
 
3.3%
Other values (70)12747
63.6%
ValueCountFrequency (%)
431
 
< 0.1%
462
 
< 0.1%
491
 
< 0.1%
501
 
< 0.1%
521
 
< 0.1%
534
 
< 0.1%
542
 
< 0.1%
556
< 0.1%
5611
0.1%
579
< 0.1%
ValueCountFrequency (%)
1391
 
< 0.1%
1261
 
< 0.1%
1251
 
< 0.1%
1242
 
< 0.1%
1231
 
< 0.1%
1224
 
< 0.1%
1212
 
< 0.1%
1207
< 0.1%
11911
0.1%
1189
< 0.1%

avg_session_minutes
Real number (ℝ)

Missing 

Distinct3845
Distinct (%)19.8%
Missing658
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean26.441482
Minimum1
Maximum58.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:29.711469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.6105
Q120.75
median26.49
Q332.11
95-th percentile40.2395
Maximum58.35
Range57.35
Interquartile range (IQR)11.36

Descriptive statistics

Standard deviation8.3910565
Coefficient of variation (CV)0.3173444
Kurtosis-0.051397901
Mean26.441482
Median Absolute Deviation (MAD)5.69
Skewness-0.00012481644
Sum512488.81
Variance70.409829
MonotonicityNot monotonic
2025-11-02T23:25:29.778327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123
 
0.1%
24.8821
 
0.1%
32.2721
 
0.1%
27.2919
 
0.1%
21.0519
 
0.1%
25.9918
 
0.1%
25.1518
 
0.1%
28.8117
 
0.1%
24.917
 
0.1%
23.7517
 
0.1%
Other values (3835)19192
95.8%
(Missing)658
 
3.3%
ValueCountFrequency (%)
123
0.1%
1.021
 
< 0.1%
1.061
 
< 0.1%
1.11
 
< 0.1%
1.121
 
< 0.1%
1.291
 
< 0.1%
1.361
 
< 0.1%
1.381
 
< 0.1%
1.561
 
< 0.1%
1.671
 
< 0.1%
ValueCountFrequency (%)
58.351
< 0.1%
56.931
< 0.1%
56.591
< 0.1%
55.361
< 0.1%
54.731
< 0.1%
54.331
< 0.1%
53.781
< 0.1%
53.641
< 0.1%
53.571
< 0.1%
53.281
< 0.1%

credit_score
Real number (ℝ)

High correlation  Missing 

Distinct483
Distinct (%)2.5%
Missing969
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean620.44444
Minimum334
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:29.844480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile489
Q1567
median621
Q3675
95-th percentile752
Maximum850
Range516
Interquartile range (IQR)108

Descriptive statistics

Standard deviation79.804125
Coefficient of variation (CV)0.12862413
Kurtosis-0.063200036
Mean620.44444
Median Absolute Deviation (MAD)54
Skewness-0.015334055
Sum11832496
Variance6368.6984
MonotonicityNot monotonic
2025-11-02T23:25:29.907856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
636115
 
0.6%
614115
 
0.6%
625114
 
0.6%
585113
 
0.6%
635112
 
0.6%
628111
 
0.6%
623110
 
0.5%
600108
 
0.5%
645106
 
0.5%
620104
 
0.5%
Other values (473)17963
89.6%
(Missing)969
 
4.8%
ValueCountFrequency (%)
3341
< 0.1%
3441
< 0.1%
3471
< 0.1%
3491
< 0.1%
3502
< 0.1%
3521
< 0.1%
3572
< 0.1%
3611
< 0.1%
3641
< 0.1%
3661
< 0.1%
ValueCountFrequency (%)
85042
0.2%
8494
 
< 0.1%
8482
 
< 0.1%
8472
 
< 0.1%
8462
 
< 0.1%
8451
 
< 0.1%
8442
 
< 0.1%
8432
 
< 0.1%
8421
 
< 0.1%
8414
 
< 0.1%

income
Real number (ℝ)

Missing 

Distinct18240
Distinct (%)98.8%
Missing1576
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean7532.3814
Minimum952.57
Maximum170382.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:29.972001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum952.57
5-th percentile3193.0235
Q14881.135
median6660.605
Q39004.505
95-th percentile14078.349
Maximum170382.45
Range169429.88
Interquartile range (IQR)4123.37

Descriptive statistics

Standard deviation6388.8943
Coefficient of variation (CV)0.8481905
Kurtosis454.97788
Mean7532.3814
Median Absolute Deviation (MAD)1984.31
Skewness18.151404
Sum1.3907789 × 108
Variance40817971
MonotonicityNot monotonic
2025-11-02T23:25:30.033537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170382.4520
 
0.1%
5851.043
 
< 0.1%
7017.722
 
< 0.1%
5026.272
 
< 0.1%
4593.132
 
< 0.1%
6373.572
 
< 0.1%
5664.592
 
< 0.1%
8062.132
 
< 0.1%
6496.052
 
< 0.1%
5000.462
 
< 0.1%
Other values (18230)18425
91.9%
(Missing)1576
 
7.9%
ValueCountFrequency (%)
952.571
< 0.1%
1094.491
< 0.1%
1296.61
< 0.1%
1454.431
< 0.1%
1470.781
< 0.1%
1555.371
< 0.1%
1559.441
< 0.1%
1562.131
< 0.1%
1568.131
< 0.1%
1577.041
< 0.1%
ValueCountFrequency (%)
170382.4520
0.1%
34076.491
 
< 0.1%
33417.291
 
< 0.1%
32888.261
 
< 0.1%
32587.131
 
< 0.1%
31350.921
 
< 0.1%
30887.291
 
< 0.1%
30103.681
 
< 0.1%
29274.151
 
< 0.1%
29161.191
 
< 0.1%

late_payments
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56996008
Minimum0
Maximum8
Zeros12384
Zeros (%)61.8%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:30.083432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87692432
Coefficient of variation (CV)1.5385715
Kurtosis4.012319
Mean0.56996008
Median Absolute Deviation (MAD)0
Skewness1.8298131
Sum11422
Variance0.76899626
MonotonicityNot monotonic
2025-11-02T23:25:30.128371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
012384
61.8%
14996
24.9%
21839
 
9.2%
3607
 
3.0%
4160
 
0.8%
540
 
0.2%
612
 
0.1%
81
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
012384
61.8%
14996
24.9%
21839
 
9.2%
3607
 
3.0%
4160
 
0.8%
540
 
0.2%
612
 
0.1%
71
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
71
 
< 0.1%
612
 
0.1%
540
 
0.2%
4160
 
0.8%
3607
 
3.0%
21839
 
9.2%
14996
24.9%
012384
61.8%

satisfaction_score
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
5
15689 
4
3946 
3
 
390
2
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20040
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
515689
78.3%
43946
 
19.7%
3390
 
1.9%
215
 
0.1%

Length

2025-11-02T23:25:30.179720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-02T23:25:30.215492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
515689
78.3%
43946
 
19.7%
3390
 
1.9%
215
 
0.1%

Most occurring characters

ValueCountFrequency (%)
515689
78.3%
43946
 
19.7%
3390
 
1.9%
215
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
515689
78.3%
43946
 
19.7%
3390
 
1.9%
215
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
515689
78.3%
43946
 
19.7%
3390
 
1.9%
215
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
515689
78.3%
43946
 
19.7%
3390
 
1.9%
215
 
0.1%

churned
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
14953 
1
5087 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20040
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
014953
74.6%
15087
 
25.4%

Length

2025-11-02T23:25:30.266885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-02T23:25:30.296539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
014953
74.6%
15087
 
25.4%

Most occurring characters

ValueCountFrequency (%)
014953
74.6%
15087
 
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014953
74.6%
15087
 
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014953
74.6%
15087
 
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014953
74.6%
15087
 
25.4%

defaulted_loan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
16177 
1
3863 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20040
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
016177
80.7%
13863
 
19.3%

Length

2025-11-02T23:25:30.331749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-02T23:25:30.462641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
016177
80.7%
13863
 
19.3%

Most occurring characters

ValueCountFrequency (%)
016177
80.7%
13863
 
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
016177
80.7%
13863
 
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
016177
80.7%
13863
 
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)20040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
016177
80.7%
13863
 
19.3%

next_month_spend
Real number (ℝ)

High correlation 

Distinct7365
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.867961
Minimum0
Maximum137.04
Zeros68
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size156.7 KiB
2025-11-02T23:25:30.503283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.99
Q119.12
median29.74
Q345.74
95-th percentile88.001
Maximum137.04
Range137.04
Interquartile range (IQR)26.62

Descriptive statistics

Standard deviation23.562896
Coefficient of variation (CV)0.65693437
Kurtosis1.2613828
Mean35.867961
Median Absolute Deviation (MAD)12.495
Skewness1.2462585
Sum718793.93
Variance555.21007
MonotonicityNot monotonic
2025-11-02T23:25:30.566276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
068
 
0.3%
14.6913
 
0.1%
20.4112
 
0.1%
18.4312
 
0.1%
20.4712
 
0.1%
20.2211
 
0.1%
25.5111
 
0.1%
23.8511
 
0.1%
20.411
 
0.1%
21.4311
 
0.1%
Other values (7355)19868
99.1%
ValueCountFrequency (%)
068
0.3%
0.071
 
< 0.1%
0.121
 
< 0.1%
0.151
 
< 0.1%
0.191
 
< 0.1%
0.211
 
< 0.1%
0.291
 
< 0.1%
0.331
 
< 0.1%
0.341
 
< 0.1%
0.41
 
< 0.1%
ValueCountFrequency (%)
137.041
< 0.1%
136.861
< 0.1%
136.041
< 0.1%
135.591
< 0.1%
134.731
< 0.1%
133.411
< 0.1%
133.311
< 0.1%
132.381
< 0.1%
129.341
< 0.1%
129.061
< 0.1%

review_text
Categorical

High correlation  Missing 

Distinct12
Distinct (%)0.1%
Missing204
Missing (%)1.0%
Memory size1.9 MiB
Fantastic experience from start to finish.
5214 
Love it. Features are exactly what I need.
5203 
Excellent! Fast, reliable, and great support.
5099 
Pretty satisfied with the features for the price.
1317 
Support was helpful and response time was decent.
1304 
Other values (7)
1699 

Length

Max length55
Median length42
Mean length43.364438
Min length26

Characters and Unicode

Total characters860177
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSupport was helpful and response time was decent.
2nd rowLove it. Features are exactly what I need.
3rd rowFantastic experience from start to finish.
4th rowLove it. Features are exactly what I need.
5th rowLove it. Features are exactly what I need.

Common Values

ValueCountFrequency (%)
Fantastic experience from start to finish.5214
26.0%
Love it. Features are exactly what I need.5203
26.0%
Excellent! Fast, reliable, and great support.5099
25.4%
Pretty satisfied with the features for the price.1317
 
6.6%
Support was helpful and response time was decent.1304
 
6.5%
Good service and the app is improving.1297
 
6.5%
Neither good nor bad. Meets basic expectations.135
 
0.7%
It's okay. Does the job most of the time.129
 
0.6%
Average experience so far.123
 
0.6%
Not great. Some features are buggy.8
 
< 0.1%
Other values (2)7
 
< 0.1%
(Missing)204
 
1.0%

Length

2025-11-02T23:25:30.629034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and7707
 
5.7%
features6528
 
4.8%
support6406
 
4.7%
experience5337
 
3.9%
to5214
 
3.8%
finish5214
 
3.8%
from5214
 
3.8%
fantastic5214
 
3.8%
start5214
 
3.8%
are5211
 
3.8%
Other values (54)78995
58.0%

Most occurring characters

ValueCountFrequency (%)
116418
13.5%
e105853
12.3%
t82940
 
9.6%
a66815
 
7.8%
r52498
 
6.1%
s43761
 
5.1%
i43604
 
5.1%
n37970
 
4.4%
o29884
 
3.5%
l28216
 
3.3%
Other values (31)252218
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)860177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
116418
13.5%
e105853
12.3%
t82940
 
9.6%
a66815
 
7.8%
r52498
 
6.1%
s43761
 
5.1%
i43604
 
5.1%
n37970
 
4.4%
o29884
 
3.5%
l28216
 
3.3%
Other values (31)252218
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)860177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
116418
13.5%
e105853
12.3%
t82940
 
9.6%
a66815
 
7.8%
r52498
 
6.1%
s43761
 
5.1%
i43604
 
5.1%
n37970
 
4.4%
o29884
 
3.5%
l28216
 
3.3%
Other values (31)252218
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)860177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
116418
13.5%
e105853
12.3%
t82940
 
9.6%
a66815
 
7.8%
r52498
 
6.1%
s43761
 
5.1%
i43604
 
5.1%
n37970
 
4.4%
o29884
 
3.5%
l28216
 
3.3%
Other values (31)252218
29.3%

Interactions

2025-11-02T23:25:25.414476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:10.286605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:11.371554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:12.213241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:13.270714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:14.234581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:15.256784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:16.202343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-02T23:25:11.171901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:12.053340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:13.107212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:14.017000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:14.998841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:16.027949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:16.967392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:18.044648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:18.988461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:20.011292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:20.975545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:21.932311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:23.069848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:24.075625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:25.201799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:26.261398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:11.236902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:12.106533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:13.161564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:14.110709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:15.143809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:16.087893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:17.020445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:18.128205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:19.040739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:20.072190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:21.034462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:22.004486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:23.138644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:24.149614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:25.286207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:26.326891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:11.306527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:12.159749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:13.215641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:14.175866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:15.200213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:16.146142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:17.084882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:18.194874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:19.096657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:20.128501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:21.091805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:22.066732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:23.193471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:24.223935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-02T23:25:25.349863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-02T23:25:30.685498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ageavg_session_minutescalls_per_monthchurnedcontractcredit_scorecustomer_iddata_usage_gbdefaulted_loandevice_typegenderhas_apphas_international_planincomelatlate_paymentslngmessages_per_monthmonthly_chargesnext_month_spendpayment_methodplan_typeprovincereview_textsatisfaction_scoresupport_tickets_last_6motenure_monthstotal_charges
age1.000-0.0020.0010.0270.018-0.0020.0030.0040.0110.0000.0000.0120.0110.0050.0060.006-0.001-0.0030.004-0.0010.0000.0000.0090.0130.0090.0040.0050.008
avg_session_minutes-0.0021.0000.0780.0080.0050.0070.0070.0530.0000.0130.0120.2490.000-0.0170.002-0.0070.005-0.0050.0960.1040.0090.1130.0000.0000.011-0.041-0.0090.059
calls_per_month0.0010.0781.0000.0140.0000.0030.0030.1700.0000.0160.0180.0110.422-0.001-0.0010.003-0.007-0.0030.3110.3480.0000.4270.0050.0100.0220.004-0.0110.192
churned0.0270.0080.0141.0000.1540.0000.0050.0000.0000.0000.0000.1020.0000.0110.0000.0060.0000.0170.0220.2640.0170.0250.0000.0490.0480.0590.0000.022
contract0.0180.0050.0000.1541.0000.0000.0060.0000.0040.0000.0000.0000.0000.0000.0160.0000.0030.0000.0000.0250.0000.0000.0000.0040.0020.0000.0000.013
credit_score-0.0020.0070.0030.0000.0001.000-0.001-0.0060.1690.0010.0000.0000.015-0.009-0.000-0.541-0.0010.0110.0040.0000.0050.0080.0080.0230.0460.011-0.005-0.005
customer_id0.0030.0070.0030.0050.006-0.0011.000-0.0100.0000.0000.0000.0000.011-0.0020.0020.008-0.0080.004-0.003-0.0020.0000.0000.0000.0040.0080.006-0.013-0.013
data_usage_gb0.0040.0530.1700.0000.000-0.006-0.0101.0000.0210.0180.0220.0000.0000.0030.0030.0130.007-0.0000.1900.3280.0070.0680.0000.0280.0240.006-0.0010.123
defaulted_loan0.0110.0000.0000.0000.0040.1690.0000.0211.0000.0000.0180.0000.0000.0130.0230.1310.0070.0000.0080.0000.0000.0000.0040.0240.0280.0000.0000.015
device_type0.0000.0130.0160.0000.0000.0010.0000.0180.0001.0000.0000.0000.0000.0000.0000.0000.0000.3260.0000.0000.0000.0000.0000.0000.0000.0000.0150.000
gender0.0000.0120.0180.0000.0000.0000.0000.0220.0180.0001.0000.0030.0000.0030.0000.0000.0090.0130.0000.0050.0100.0000.0110.0050.0080.0000.0030.000
has_app0.0120.2490.0110.1020.0000.0000.0000.0000.0000.0000.0031.0000.0000.0000.0000.0000.0120.0220.0060.0100.0130.0000.0130.1330.1340.1840.0000.020
has_international_plan0.0110.0000.4220.0000.0000.0150.0110.0000.0000.0000.0000.0001.0000.0030.0000.0000.0000.0000.0110.0240.0000.0000.0000.0090.0000.0000.0000.024
income0.005-0.017-0.0010.0110.000-0.009-0.0020.0030.0130.0000.0030.0000.0031.000-0.0030.007-0.0080.0040.0100.0150.0000.0000.0000.0000.0000.001-0.0070.000
lat0.0060.002-0.0010.0000.016-0.0000.0020.0030.0230.0000.0000.0000.000-0.0031.0000.0030.339-0.001-0.0050.0010.0000.0050.6830.0100.0000.0050.003-0.000
late_payments0.006-0.0070.0030.0060.000-0.5410.0080.0130.1310.0000.0000.0000.0000.0070.0031.0000.000-0.0130.0010.0040.0000.0000.0000.0740.119-0.0130.0060.009
lng-0.0010.005-0.0070.0000.003-0.001-0.0080.0070.0070.0000.0090.0120.000-0.0080.3390.0001.000-0.005-0.004-0.0030.0160.0000.7300.0080.0070.002-0.003-0.004
messages_per_month-0.003-0.005-0.0030.0170.0000.0110.004-0.0000.0000.3260.0130.0220.0000.004-0.001-0.013-0.0051.000-0.0070.0040.0000.0000.0000.0130.011-0.002-0.006-0.007
monthly_charges0.0040.0960.3110.0220.0000.004-0.0030.1900.0080.0000.0000.0060.0110.010-0.0050.001-0.004-0.0071.0000.9060.0160.4540.0000.0210.0330.0030.0100.611
next_month_spend-0.0010.1040.3480.2640.0250.000-0.0020.3280.0000.0000.0050.0100.0240.0150.0010.004-0.0030.0040.9061.0000.0000.8100.0000.0260.041-0.0020.0110.563
payment_method0.0000.0090.0000.0170.0000.0050.0000.0070.0000.0000.0100.0130.0000.0000.0000.0000.0160.0000.0160.0001.0000.0000.0140.0000.0000.0100.0000.011
plan_type0.0000.1130.4270.0250.0000.0080.0000.0680.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.4540.8100.0001.0000.0000.0280.0260.0000.0000.496
province0.0090.0000.0050.0000.0000.0080.0000.0000.0040.0000.0110.0130.0000.0000.6830.0000.7300.0000.0000.0000.0140.0001.0000.0080.0070.0000.0000.000
review_text0.0130.0000.0100.0490.0040.0230.0040.0280.0240.0000.0050.1330.0090.0000.0100.0740.0080.0130.0210.0260.0000.0280.0081.0001.0000.1510.0050.000
satisfaction_score0.0090.0110.0220.0480.0020.0460.0080.0240.0280.0000.0080.1340.0000.0000.0000.1190.0070.0110.0330.0410.0000.0260.0071.0001.0000.2460.0000.015
support_tickets_last_6mo0.004-0.0410.0040.0590.0000.0110.0060.0060.0000.0000.0000.1840.0000.0010.005-0.0130.002-0.0020.003-0.0020.0100.0000.0000.1510.2461.000-0.003-0.003
tenure_months0.005-0.009-0.0110.0000.000-0.005-0.013-0.0010.0000.0150.0030.0000.000-0.0070.0030.006-0.003-0.0060.0100.0110.0000.0000.0000.0050.000-0.0031.0000.744
total_charges0.0080.0590.1920.0220.013-0.005-0.0130.1230.0150.0000.0000.0200.0240.000-0.0000.009-0.004-0.0070.6110.5630.0110.4960.0000.0000.015-0.0030.7441.000

Missing values

2025-11-02T23:25:26.443164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-02T23:25:26.600111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-02T23:25:26.833034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

customer_idsignup_datelast_seenagegenderprovincelatlngplan_typecontractpayment_methoddevice_typehas_apphas_international_plantenure_monthsmonthly_chargestotal_chargessupport_tickets_last_6modata_usage_gbcalls_per_monthmessages_per_monthavg_session_minutescredit_scoreincomelate_paymentssatisfaction_scorechurneddefaulted_loannext_month_spendreview_text
023812023-10-042025-07-0137MaleMatabeleland North-18.503727.6997PremiumMonth-to-MonthNaNAndroid102148.99986.33317.01597626.40538.04368.38140059.47Support was helpful and response time was decent.
187442018-05-182020-11-2227FemaleMashonaland West-17.179130.2245PrepaidMonth-to-MonthCredit CardAndroid10308.88260.98011.933910832.68607.013421.7105009.73Love it. Features are exactly what I need.
2140952022-11-112023-11-0128MaleMidlands-19.916229.9921PrepaidOne YearEcoCashAndroid101114.10169.3003.66418917.35667.0NaN150024.18Fantastic experience from start to finish.
328602019-03-242020-05-0337MaleBulawayo-20.140728.6429PrepaidMonth-to-MonthEcoCashAndroid101315.42186.6708.53319230.85486.08896.62250122.16Love it. Features are exactly what I need.
4179462020-02-232020-10-1218FemaleMatabeleland South-20.794029.4687PostpaidTwo YearDebit CardiOS11734.55244.7928.83446424.68670.06673.12050044.83Love it. Features are exactly what I need.
581392021-10-092024-08-0225FemaleMashonaland Central-16.732631.0045PostpaidMonth-to-MonthEcoCashiOS103428.951010.0125.44458541.02525.0NaN350037.43Fantastic experience from start to finish.
652492021-03-132022-01-3125MaleMatabeleland North-18.470027.5706PrepaidOne YearEcoCashAndroid101014.39133.1604.83448131.67774.09734.4305003.53Fantastic experience from start to finish.
713382022-10-052025-03-1243MaleMashonaland East-18.280732.3509PostpaidOne YearCashAndroid102927.49804.2709.88389826.80685.04036.29050039.01Love it. Features are exactly what I need.
812912020-10-112021-05-2836FemaleBulawayo-19.777428.3943PostpaidMonth-to-MonthEcoCashiOS10725.18164.46014.14448125.78664.05493.90050034.90Love it. Features are exactly what I need.
910072022-11-052022-11-1051FemaleBulawayo-19.793728.7199PremiumOne YearEcoCashiOS00081.800.00111.425885NaN645.03394.14050098.00Fantastic experience from start to finish.
customer_idsignup_datelast_seenagegenderprovincelatlngplan_typecontractpayment_methoddevice_typehas_apphas_international_plantenure_monthsmonthly_chargestotal_chargessupport_tickets_last_6modata_usage_gbcalls_per_monthmessages_per_monthavg_session_minutescredit_scoreincomelate_paymentssatisfaction_scorechurneddefaulted_loannext_month_spendreview_text
20030113642020-08-082020-09-0540FemaleMashonaland Central-16.162731.3112PrepaidMonth-to-MonthCredit CardAndroid11018.670.0013.68538648.16514.05597.67050114.45Love it. Features are exactly what I need.
20031144242018-09-122020-06-2344MaleMatabeleland North-18.405227.4453PrepaidMonth-to-MonthCredit CardAndroid11217.83176.83026.72678517.97445.07181.52240024.11Pretty satisfied with the features for the price.
2003244272019-08-292020-06-2030MaleMatabeleland South-20.839429.0095PrepaidMonth-to-MonthDebit CardWeb10919.24175.4204.10397619.42695.010955.67050016.13Fantastic experience from start to finish.
20033168512024-04-112025-06-2427MaleMatabeleland North-18.457227.4358PremiumOne YearDebit CardAndroid111475.071081.54022.90749832.02460.02977.49230092.53It's okay. Does the job most of the time.
2003462662021-07-042023-11-2633MaleMidlands-19.326229.7608PrepaidOne YearDebit CardAndroid11297.98230.0117.695010133.77680.06982.97040010.53Support was helpful and response time was decent.
20035112852024-02-272025-09-3051MaleMashonaland Central-16.771130.6232PostpaidMonth-to-MonthDebit CardAndroid101932.30631.0909.21378124.74533.0NaN150042.12Excellent! Fast, reliable, and great support.
20036119652021-11-012023-02-0618FemaleMashonaland East-18.450132.2472PostpaidMonth-to-MonthDebit CardAndroid101538.52615.73010.32388024.47521.013032.40051047.78Excellent! Fast, reliable, and great support.
2003753912018-12-232019-06-0824FemaleMashonaland Central-16.861531.1293PostpaidMonth-to-MonthCredit CardAndroid10537.11197.2706.20289033.28633.05464.86050038.08Love it. Features are exactly what I need.
200388612023-06-062024-08-0134FemaleMashonaland West-17.463930.5270PostpaidOne YearDebit CardAndroid101425.77355.52018.124510120.72493.0NaN151027.89Love it. Features are exactly what I need.
20039157962019-06-222022-05-1531FemaleHarare-17.788231.0157PostpaidOne YearDebit CardAndroid103539.451379.02115.85328930.82684.03645.68050150.08Love it. Features are exactly what I need.

Duplicate rows

Most frequently occurring

customer_idsignup_datelast_seenagegenderprovincelatlngplan_typecontractpayment_methoddevice_typehas_apphas_international_plantenure_monthsmonthly_chargestotal_chargessupport_tickets_last_6modata_usage_gbcalls_per_monthmessages_per_monthavg_session_minutescredit_scoreincomelate_paymentssatisfaction_scorechurneddefaulted_loannext_month_spendreview_text# duplicates
012042020-04-072020-12-2831MaleManicaland-19.345132.5371PostpaidMonth-to-MonthCashiOS10831.72237.4403.96387728.81434.05948.24150029.11Love it. Features are exactly what I need.2
125592018-07-162019-09-1239FemaleMashonaland Central-16.545031.1915PrepaidOne YearDebit CardiOS101413.02196.51113.48438217.42600.0NaN050124.76Excellent! Fast, reliable, and great support.2
227452022-05-102024-08-2747MaleMashonaland Central-16.543231.1210PrepaidMonth-to-MonthEcoCashWeb102817.90519.6729.03227517.47635.04182.88050017.50Fantastic experience from start to finish.2
331162024-02-212025-09-3035FemaleMashonaland West-17.149030.7169PrepaidMonth-to-MonthNaNAndroid001912.66249.821NaN3610111.25778.03754.9105105.72Love it. Features are exactly what I need.2
434392024-06-302025-09-3042FemaleMatabeleland South-21.001128.5506PremiumMonth-to-MonthDebit CardWeb101580.931209.35011.765880NaN542.08062.13050195.82Excellent! Fast, reliable, and great support.2
537812020-10-182021-05-0131FemaleMatabeleland North-18.538327.5517PostpaidOne YearEcoCashiOS10633.70185.7413.88436421.05562.06714.57251030.97Fantastic experience from start to finish.2
653482019-10-212022-01-0420MaleBulawayo-20.061028.6791PrepaidMonth-to-MonthDebit CardWeb102616.91448.6015.71466222.81690.07739.48050022.26Love it. Features are exactly what I need.2
763002020-03-072020-08-2720FemaleBulawayo-20.209828.6688PremiumMonth-to-MonthEcoCashiOS00559.14298.77110.05407832.19589.03570.65050069.37Fantastic experience from start to finish.2
867212023-05-282023-06-0431MaleMatabeleland North-18.599227.7133PrepaidMonth-to-MonthDebit CardAndroid00020.240.00213.374110919.12NaN7266.04051019.03Fantastic experience from start to finish.2
967492024-12-122025-09-3038MaleMashonaland West-17.366630.2558PremiumMonth-to-MonthCredit CardAndroid10975.35649.41019.09518931.67532.01869.06040095.46Support was helpful and response time was decent.2